In this paper, we develop a modeling framework to investigate how execution error impacts strategy. With direct control over execution error, we can investigate how particular magnitudes and types of execution error affect the value of different game states and the selection of optimal actions. We illustrate our framework within the context of tennis, which conveniently provides a concrete, two-dimensional action space to visualize errors in executing particular shots. In our modeling framework, optimality will be measured with respect to a player winning the point, which is compatible with a rational player's real-world objective. Our modeling framework uses a dynamic programming-based value function. We develop two models, an infinite horizon Markov reward process (MRP) and an infinite horizon Markov decision process (MDP) [1]}. The MRP is used to compute the value of particular shot selection strategies, while the MDP is used to find an optimal strategy, each for a particular level of execution error.
